Usually, a spline is constructed from some information, like
function values and/or derivative values, or as the approximate solution
of some ordinary differential equation. But it is also possible to
make up a spline from scratch, by providing its knot sequence and
its coefficient sequence to the command `spmak`

.

For example, if you enter

sp = spmak(1:10,3:8);

you supply the uniform knot sequence `1:10`

and
the coefficient sequence `3:8`

. Because there are
10 knots and 6 coefficients, the order must be 4(= 10 – 6),
i.e., you get a cubic spline. The command

fnbrk(sp)

prints out the constituent parts of the B-form of this cubic spline, as follows:

knots(1:n+k) 1 2 3 4 5 6 7 8 9 10 coefficients(d,n) 3 4 5 6 7 8 number n of coefficients 6 order k 4 dimension d of target 1

Further, `fnbrk`

can be used to supply each
of these parts separately.

But the point of the Curve Fitting Toolbox™ spline functionality
is that there shouldn't be any need for you to look up these details.
You simply use `sp`

as an argument to commands that
evaluate, differentiate, integrate, convert, or plot the spline whose
description is contained in `sp`

.

The following commands are available for spline work. There
is `spmak`

and `fnbrk`

to make up a spline and take it
apart again. Use `fn2fm`

to
convert from B-form to ppform. You can evaluate, differentiate, integrate,
minimize, find zeros of, plot, refine, or selectively extrapolate
a spline with the aid of `fnval`

, `fnder`

, `fndir`

, `fnint`

, `fnmin`

, `fnzeros`

, `fnplt`

, `fnrfn`

,
and `fnxtr`

.

There are five commands for generating knot sequences:

`augknt`

for providing boundary knots and also controlling the multiplicity of interior knots`brk2knt`

for supplying a knot sequence with specified multiplicities`aptknt`

for providing a knot sequence for a spline space of given order that is suitable for interpolation at given data sites`optknt`

for providing an*optimal*knot sequence for interpolation at given sites`newknt`

for a knot sequence perhaps more suitable for the function to be approximated

In addition, there is:

`aveknt`

to supply certain knot averages (the Greville sites) as recommended sites for interpolation`chbpnt`

to supply such sites`knt2brk`

and`knt2mlt`

for extracting the breaks and/or their multiplicities from a given knot sequence

To display a spline *curve* with given two-dimensional
coefficient sequence and a uniform knot sequence, use `spcrv`

.

You can also write your own spline construction commands, in
which case you will need to know the following. The construction of
a spline satisfying some interpolation or approximation conditions
usually requires a *collocation matrix*,
i.e., the matrix that, in each row, contains the sequence of numbers *D*^{r}*B*_{j,k}(τ),
i.e., the *r*th derivative at τ of the *j*th
B-spline, for all *j*, for some *r* and
some site τ. Such a matrix is provided by `spcol`

. An optional argument allows for this
matrix to be supplied by `spcol`

in a space-saving
spline-almost-block-diagonal-form or as a MATLAB^{®} sparse matrix.
It can be fed to `slvblk`

, a command for solving
linear systems with an almost-block-diagonal coefficient matrix. If
you are interested in seeing how `spcol`

and `slvblk`

are
used in this toolbox, have a look at the commands `spapi`

, `spap2`

,
and `spaps`

.

In addition, there are routines for constructing *cubic* splines. `csapi`

and `csape`

provide
the cubic spline interpolant at knots to given data, using the not-a-knot
and various other end conditions, respectively. A parametric cubic
spline curve through given points is provided by `cscvn`

.
The cubic *smoothing* spline is constructed in `csaps`

.

As another simple example,

points = .95*[0 -1 0 1;1 0 -1 0]; sp = spmak(-4:8,[points points]);

provides a planar, quartic, spline curve whose middle part is a pretty good approximation to a circle, as the plot on the next page shows. It is generated by a subsequent

plot(points(1,:),points(2,:),'x'), hold on fnplt(sp,[0,4]), axis equal square, hold off

Insertion of additional control points $$\left(\pm 0.95,\pm 0.95\right)/\sqrt{1.9}$$ would make a visually perfect circle.

Here are more details. The spline curve
generated has the form Σ^{8}_{j=1}B_{j,5}*a*(:,* j*),
with -`4:8 `

the uniform knot sequence, and with its
control points *a*(:,*j*) the sequence (0,α),(–α,0),(0,–α),(α,0),(0,α),(–α,0),(0,–α),(α,0) with α=0.95.
Only the curve part between the parameter values 0 and 4 is actually
plotted.

To get a feeling for how close to circular this part of the
curve actually is, compute its unsigned curvature. The curvature κ(*t*)
at the curve point γ(*t*) = (x(*t*),
y(*t*)) of a space curve γ can be computed
from the formula

$$\kappa =\frac{\left|x\text{'}y\text{'}\text{'}-y\text{'}x\text{'}\text{'}\right|}{{(x{\text{'}}^{2}+y{\text{'}}^{2})}^{3/2}}$$

in which x', x″, y', and y" are the first and
second derivatives of the curve with respect to the parameter used
(*t*). Treat the planar curve as a space curve in
the (*x*,*y*)-plane, hence obtain
the maximum and minimum of its curvature at 21 points as follows:

t = linspace(0,4,21);zt = zeros(size(t)); dsp = fnder(sp); dspt = fnval(dsp,t); ddspt = fnval(fnder(dsp),t); kappa = abs(dspt(1,:).*ddspt(2,:)-dspt(2,:).*ddspt(1,:))./... (sum(dspt.^2)).^(3/2); [min(kappa),max(kappa)] ans = 1.6747 1.8611

So, while the curvature is not quite constant, it is close to 1/radius of the circle, as you see from the next calculation:

1/norm(fnval(sp,0)) ans = 1.7864

**Spline Approximation to a Circle; Control Points
Are Marked x**

Was this topic helpful?